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657499dbab
picksplit algorithm for Rtree. Please apply patch below to CVS Oleg Bartunov
99 lines
3.2 KiB
Plaintext
99 lines
3.2 KiB
Plaintext
This is R-Tree implementation using GiST.
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Code (for PG95) are taken from http://s2k-ftp.cs.berkeley.edu:8000/gist/pggist/
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and changed according to new version of GiST (7.1 and above)
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All work was done by Teodor Sigaev (teodor@stack.net) and Oleg Bartunov
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(oleg@sai.msu.su). See http://www.sai.msu.su/~megera/postgres/gist
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for additional information.
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CHANGES:
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Oct 10 MSD 2001
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1. Implemented new linear algorithm for picksplit
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ref. ( 'New Linear Node Splitting Algorithm for R-tree',
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C.H.Ang and T.C.Tan )
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Tue May 29 17:04:16 MSD 2001
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1. Small fixes in polygon code
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Thanks to Dave Blasby <dblasby@refractions.net>
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Mon May 28 19:42:14 MSD 2001
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1. Full implementation of R-tree using GiST - gist_box_ops,gist_poly_ops
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2. gist_poly_ops is lossy
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3. NULLs support
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4. works with multi-key GiST
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NOTICE:
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This version will works only with postgresql version 7.1 and above
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because of changes in interface of function calling.
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INSTALLATION:
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gmake
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gmake install
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-- load functions
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psql <database> < rtree_gist.sql
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REGRESSION TEST:
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gmake installcheck
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EXAMPLE USAGE:
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create table boxtmp (b box);
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-- create index
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create index bix on boxtmp using gist (b gist_box_ops);
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-- query
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select * from boxtmp where b && '(1000,1000,0,0)'::box;
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BENCHMARKS:
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subdirectory bench contains benchmark suite.
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Prerequisities: perl, DBI, DBD:Pg, Time::HiRes
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cd ./bench
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1. createdb TEST
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2. psql TEST < ../box.sql
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3. ./create_test.pl | psql TEST
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-- change $NUM - number of rows in test dataset
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4. ./bench.pl - perl script to benchmark queries.
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Run script without arguments to see available options.
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a)test without GiST index, using built-in R-Tree
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./bench.pl -d TEST
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b)test R-Tree using GiST index
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./bench.pl -d TEST -g
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RESULTS:
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1. One interesting thing is that insertion time for built-in R-Tree is
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about 8 times more than ones for GiST implementation of R-Tree !!!
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2. Postmaster requires much more memory for built-in R-Tree
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3. Search time depends on dataset. In our case we got:
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+------------+-----------+--------------+
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|Number boxes|R-tree, sec|R-tree using |
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| | | GiST, sec |
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+------------+-----------+--------------+
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| 10| 0.002| 0.002|
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+------------+-----------+--------------+
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| 100| 0.002| 0.002|
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+------------+-----------+--------------+
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| 1000| 0.002| 0.002|
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+------------+-----------+--------------+
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| 10000| 0.015| 0.025|
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+------------+-----------+--------------+
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| 20000| 0.029| 0.048|
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+------------+-----------+--------------+
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| 40000| 0.055| 0.092|
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+------------+-----------+--------------+
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| 80000| 0.113| 0.178|
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+------------+-----------+--------------+
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| 160000| 0.338| 0.337|
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+------------+-----------+--------------+
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| 320000| 0.674| 0.673|
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+------------+-----------+--------------+
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